Overview

Dataset statistics

Number of variables31
Number of observations150000
Missing cells43492
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.5 MiB
Average record size in memory248.0 B

Variable types

Numeric27
Categorical4

Alerts

offerType has constant value "0" Constant
name is highly correlated with v_1 and 3 other fieldsHigh correlation
regDate is highly correlated with price and 5 other fieldsHigh correlation
power is highly correlated with price and 2 other fieldsHigh correlation
price is highly correlated with regDate and 7 other fieldsHigh correlation
v_0 is highly correlated with regDate and 8 other fieldsHigh correlation
v_1 is highly correlated with name and 3 other fieldsHigh correlation
v_2 is highly correlated with regDate and 5 other fieldsHigh correlation
v_3 is highly correlated with regDate and 6 other fieldsHigh correlation
v_4 is highly correlated with v_5 and 2 other fieldsHigh correlation
v_5 is highly correlated with power and 4 other fieldsHigh correlation
v_6 is highly correlated with name and 3 other fieldsHigh correlation
v_7 is highly correlated with v_2High correlation
v_8 is highly correlated with regDate and 5 other fieldsHigh correlation
v_9 is highly correlated with v_4 and 2 other fieldsHigh correlation
v_10 is highly correlated with name and 4 other fieldsHigh correlation
v_11 is highly correlated with name and 4 other fieldsHigh correlation
v_12 is highly correlated with regDate and 5 other fieldsHigh correlation
v_13 is highly correlated with v_4 and 2 other fieldsHigh correlation
name is highly correlated with v_1 and 2 other fieldsHigh correlation
regDate is highly correlated with price and 4 other fieldsHigh correlation
model is highly correlated with v_14High correlation
price is highly correlated with regDate and 4 other fieldsHigh correlation
v_0 is highly correlated with regDate and 7 other fieldsHigh correlation
v_1 is highly correlated with name and 2 other fieldsHigh correlation
v_2 is highly correlated with v_5 and 3 other fieldsHigh correlation
v_3 is highly correlated with regDate and 4 other fieldsHigh correlation
v_4 is highly correlated with v_9 and 1 other fieldsHigh correlation
v_5 is highly correlated with v_0 and 3 other fieldsHigh correlation
v_6 is highly correlated with name and 2 other fieldsHigh correlation
v_7 is highly correlated with v_0 and 3 other fieldsHigh correlation
v_8 is highly correlated with regDate and 4 other fieldsHigh correlation
v_9 is highly correlated with v_4 and 1 other fieldsHigh correlation
v_10 is highly correlated with name and 3 other fieldsHigh correlation
v_11 is highly correlated with v_0 and 3 other fieldsHigh correlation
v_12 is highly correlated with regDate and 4 other fieldsHigh correlation
v_13 is highly correlated with v_4 and 1 other fieldsHigh correlation
v_14 is highly correlated with modelHigh correlation
regDate is highly correlated with price and 3 other fieldsHigh correlation
price is highly correlated with regDate and 4 other fieldsHigh correlation
v_0 is highly correlated with price and 3 other fieldsHigh correlation
v_1 is highly correlated with v_6 and 1 other fieldsHigh correlation
v_2 is highly correlated with v_12High correlation
v_3 is highly correlated with regDate and 4 other fieldsHigh correlation
v_4 is highly correlated with v_5 and 2 other fieldsHigh correlation
v_5 is highly correlated with v_4 and 1 other fieldsHigh correlation
v_6 is highly correlated with v_1 and 1 other fieldsHigh correlation
v_8 is highly correlated with regDate and 3 other fieldsHigh correlation
v_9 is highly correlated with v_4 and 2 other fieldsHigh correlation
v_10 is highly correlated with v_0 and 2 other fieldsHigh correlation
v_12 is highly correlated with regDate and 5 other fieldsHigh correlation
v_13 is highly correlated with v_4 and 1 other fieldsHigh correlation
seller is highly correlated with notRepairedDamage and 1 other fieldsHigh correlation
notRepairedDamage is highly correlated with seller and 1 other fieldsHigh correlation
gearbox is highly correlated with offerTypeHigh correlation
offerType is highly correlated with seller and 2 other fieldsHigh correlation
name is highly correlated with v_1 and 2 other fieldsHigh correlation
regDate is highly correlated with kilometer and 6 other fieldsHigh correlation
model is highly correlated with brand and 1 other fieldsHigh correlation
brand is highly correlated with model and 2 other fieldsHigh correlation
bodyType is highly correlated with v_5 and 1 other fieldsHigh correlation
kilometer is highly correlated with regDateHigh correlation
price is highly correlated with regDate and 5 other fieldsHigh correlation
v_0 is highly correlated with regDate and 9 other fieldsHigh correlation
v_1 is highly correlated with name and 3 other fieldsHigh correlation
v_2 is highly correlated with v_0 and 5 other fieldsHigh correlation
v_3 is highly correlated with regDate and 10 other fieldsHigh correlation
v_4 is highly correlated with brand and 5 other fieldsHigh correlation
v_5 is highly correlated with bodyType and 11 other fieldsHigh correlation
v_6 is highly correlated with name and 3 other fieldsHigh correlation
v_7 is highly correlated with v_0 and 4 other fieldsHigh correlation
v_8 is highly correlated with regDate and 6 other fieldsHigh correlation
v_9 is highly correlated with model and 4 other fieldsHigh correlation
v_10 is highly correlated with name and 12 other fieldsHigh correlation
v_11 is highly correlated with bodyType and 10 other fieldsHigh correlation
v_12 is highly correlated with regDate and 8 other fieldsHigh correlation
v_13 is highly correlated with v_3 and 4 other fieldsHigh correlation
v_14 is highly correlated with v_3 and 3 other fieldsHigh correlation
bodyType has 4506 (3.0%) missing values Missing
fuelType has 8680 (5.8%) missing values Missing
gearbox has 5981 (4.0%) missing values Missing
notRepairedDamage has 24324 (16.2%) missing values Missing
power is highly skewed (γ1 = 65.86317787) Skewed
creatDate is highly skewed (γ1 = -79.01331042) Skewed
SaleID is uniformly distributed Uniform
SaleID has unique values Unique
model has 11762 (7.8%) zeros Zeros
brand has 31480 (21.0%) zeros Zeros
bodyType has 41420 (27.6%) zeros Zeros
fuelType has 91656 (61.1%) zeros Zeros
power has 12829 (8.6%) zeros Zeros
v_5 has 4485 (3.0%) zeros Zeros
v_6 has 35465 (23.6%) zeros Zeros
v_7 has 5467 (3.6%) zeros Zeros
v_8 has 1597 (1.1%) zeros Zeros
v_9 has 3486 (2.3%) zeros Zeros

Reproduction

Analysis started2021-12-18 05:17:53.653740
Analysis finished2021-12-18 05:23:03.503024
Duration5 minutes and 9.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

SaleID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct150000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74999.5
Minimum0
Maximum149999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:03.722411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7499.95
Q137499.75
median74999.5
Q3112499.25
95-th percentile142499.05
Maximum149999
Range149999
Interquartile range (IQR)74999.5

Descriptive statistics

Standard deviation43301.41453
Coefficient of variation (CV)0.5773560427
Kurtosis-1.2
Mean74999.5
Median Absolute Deviation (MAD)37500
Skewness0
Sum1.1249925 × 1010
Variance1875012500
MonotonicityStrictly increasing
2021-12-17T21:23:03.955832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1000121
 
< 0.1%
999961
 
< 0.1%
999971
 
< 0.1%
999981
 
< 0.1%
999991
 
< 0.1%
1000001
 
< 0.1%
1000011
 
< 0.1%
1000021
 
< 0.1%
1000031
 
< 0.1%
Other values (149990)149990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1499991
< 0.1%
1499981
< 0.1%
1499971
< 0.1%
1499961
< 0.1%
1499951
< 0.1%
1499941
< 0.1%
1499931
< 0.1%
1499921
< 0.1%
1499911
< 0.1%
1499901
< 0.1%

name
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99662
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68349.17287
Minimum0
Maximum196812
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:04.210152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile811
Q111156
median51638
Q3118841.25
95-th percentile180617.05
Maximum196812
Range196812
Interquartile range (IQR)107685.25

Descriptive statistics

Standard deviation61103.87509
Coefficient of variation (CV)0.8939958236
Kurtosis-1.039944629
Mean68349.17287
Median Absolute Deviation (MAD)46018
Skewness0.5576057626
Sum1.025237593 × 1010
Variance3733683552
MonotonicityNot monotonic
2021-12-17T21:23:04.482036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
708282
 
0.2%
387282
 
0.2%
55280
 
0.2%
1541263
 
0.2%
203233
 
0.2%
53221
 
0.1%
713217
 
0.1%
290197
 
0.1%
1186184
 
0.1%
911182
 
0.1%
Other values (99652)147659
98.4%
ValueCountFrequency (%)
04
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
635
< 0.1%
71
 
< 0.1%
96
 
< 0.1%
1022
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
146
 
< 0.1%
ValueCountFrequency (%)
1968121
< 0.1%
1968111
< 0.1%
1968101
< 0.1%
1968091
< 0.1%
1968071
< 0.1%
1968001
< 0.1%
1967961
< 0.1%
1967951
< 0.1%
1967941
< 0.1%
1967931
< 0.1%

regDate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3894
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20034170.51
Minimum19910001
Maximum20151212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:04.742959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19910001
5-th percentile19950209
Q119990912
median20030912
Q320071109
95-th percentile20120811
Maximum20151212
Range241211
Interquartile range (IQR)80197

Descriptive statistics

Standard deviation53649.87926
Coefficient of variation (CV)0.00267791867
Kurtosis-0.6973078263
Mean20034170.51
Median Absolute Deviation (MAD)40096
Skewness0.02849507907
Sum3.005125577 × 1012
Variance2878309544
MonotonicityNot monotonic
2021-12-17T21:23:05.021832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000008180
 
0.1%
20000011158
 
0.1%
20000010157
 
0.1%
20000004157
 
0.1%
20000002155
 
0.1%
20000009154
 
0.1%
20000005151
 
0.1%
20000001149
 
0.1%
20000006143
 
0.1%
20000007142
 
0.1%
Other values (3884)148454
99.0%
ValueCountFrequency (%)
1991000114
< 0.1%
1991000214
< 0.1%
1991000312
< 0.1%
1991000411
< 0.1%
1991000515
< 0.1%
1991000615
< 0.1%
1991000718
< 0.1%
1991000815
< 0.1%
1991000915
< 0.1%
1991001015
< 0.1%
ValueCountFrequency (%)
201512121
 
< 0.1%
201512113
 
< 0.1%
201512105
< 0.1%
201512091
 
< 0.1%
201512084
< 0.1%
201512078
< 0.1%
201512066
< 0.1%
201512054
< 0.1%
201512042
 
< 0.1%
201512036
< 0.1%

model
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct248
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47.12902086
Minimum0
Maximum247
Zeros11762
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:05.259739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median30
Q366
95-th percentile162
Maximum247
Range247
Interquartile range (IQR)56

Descriptive statistics

Standard deviation49.53603965
Coefficient of variation (CV)1.051072964
Kurtosis1.740483202
Mean47.12902086
Median Absolute Deviation (MAD)24
Skewness1.484387653
Sum7069306
Variance2453.819225
MonotonicityNot monotonic
2021-12-17T21:23:05.506221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011762
 
7.8%
199573
 
6.4%
48445
 
5.6%
16038
 
4.0%
295186
 
3.5%
485052
 
3.4%
404502
 
3.0%
264496
 
3.0%
84391
 
2.9%
313827
 
2.6%
Other values (238)86727
57.8%
ValueCountFrequency (%)
011762
7.8%
16038
4.0%
2286
 
0.2%
3920
 
0.6%
48445
5.6%
52063
 
1.4%
6301
 
0.2%
71460
 
1.0%
84391
 
2.9%
955
 
< 0.1%
ValueCountFrequency (%)
2471
 
< 0.1%
2467
 
< 0.1%
2452
 
< 0.1%
2443
 
< 0.1%
2434
 
< 0.1%
2422
 
< 0.1%
24110
 
< 0.1%
2402
 
< 0.1%
23914
 
< 0.1%
23844
< 0.1%

brand
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.052733333
Minimum0
Maximum39
Zeros31480
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:05.726350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q313
95-th percentile25
Maximum39
Range39
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.864956341
Coefficient of variation (CV)0.9766815832
Kurtosis1.076201475
Mean8.052733333
Median Absolute Deviation (MAD)5
Skewness1.150760283
Sum1207910
Variance61.85753825
MonotonicityNot monotonic
2021-12-17T21:23:05.990741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
031480
21.0%
416737
11.2%
1416089
10.7%
1014249
9.5%
113794
9.2%
610217
 
6.8%
97306
 
4.9%
54665
 
3.1%
133817
 
2.5%
112945
 
2.0%
Other values (30)28701
19.1%
ValueCountFrequency (%)
031480
21.0%
113794
9.2%
2321
 
0.2%
32461
 
1.6%
416737
11.2%
54665
 
3.1%
610217
 
6.8%
72361
 
1.6%
82077
 
1.4%
97306
 
4.9%
ValueCountFrequency (%)
399
 
< 0.1%
3865
 
< 0.1%
37333
 
0.2%
36228
 
0.2%
35180
 
0.1%
34227
 
0.2%
33218
 
0.1%
32592
0.4%
31318
 
0.2%
30940
0.6%

bodyType
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing4506
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1.792369445
Minimum0
Maximum7
Zeros41420
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:06.251007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.760639503
Coefficient of variation (CV)0.9822972091
Kurtosis0.2069369316
Mean1.792369445
Median Absolute Deviation (MAD)1
Skewness0.991529939
Sum260779
Variance3.099851461
MonotonicityNot monotonic
2021-12-17T21:23:06.520278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
041420
27.6%
135272
23.5%
230324
20.2%
313491
 
9.0%
49609
 
6.4%
57607
 
5.1%
66482
 
4.3%
71289
 
0.9%
(Missing)4506
 
3.0%
ValueCountFrequency (%)
041420
27.6%
135272
23.5%
230324
20.2%
313491
 
9.0%
49609
 
6.4%
57607
 
5.1%
66482
 
4.3%
71289
 
0.9%
ValueCountFrequency (%)
71289
 
0.9%
66482
 
4.3%
57607
 
5.1%
49609
 
6.4%
313491
 
9.0%
230324
20.2%
135272
23.5%
041420
27.6%

fuelType
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8680
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean0.3758420606
Minimum0
Maximum6
Zeros91656
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:06.780158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5486766226
Coefficient of variation (CV)1.459859553
Kurtosis5.8800487
Mean0.3758420606
Median Absolute Deviation (MAD)0
Skewness1.595485994
Sum53114
Variance0.3010460362
MonotonicityNot monotonic
2021-12-17T21:23:06.949424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
091656
61.1%
146991
31.3%
22212
 
1.5%
3262
 
0.2%
4118
 
0.1%
545
 
< 0.1%
636
 
< 0.1%
(Missing)8680
 
5.8%
ValueCountFrequency (%)
091656
61.1%
146991
31.3%
22212
 
1.5%
3262
 
0.2%
4118
 
0.1%
545
 
< 0.1%
636
 
< 0.1%
ValueCountFrequency (%)
636
 
< 0.1%
545
 
< 0.1%
4118
 
0.1%
3262
 
0.2%
22212
 
1.5%
146991
31.3%
091656
61.1%

gearbox
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5981
Missing (%)4.0%
Memory size1.1 MiB
0.0
111623 
1.0
32396 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0111623
74.4%
1.032396
 
21.6%
(Missing)5981
 
4.0%

Length

2021-12-17T21:23:07.150041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T21:23:07.271238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0111623
77.5%
1.032396
 
22.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

power
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct566
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.3165467
Minimum0
Maximum19312
Zeros12829
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:07.460224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175
median110
Q3150
95-th percentile232
Maximum19312
Range19312
Interquartile range (IQR)75

Descriptive statistics

Standard deviation177.1684192
Coefficient of variation (CV)1.484860433
Kurtosis5733.451054
Mean119.3165467
Median Absolute Deviation (MAD)37
Skewness65.86317787
Sum17897482
Variance31388.64875
MonotonicityNot monotonic
2021-12-17T21:23:07.701684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012829
 
8.6%
759593
 
6.4%
1506495
 
4.3%
606374
 
4.2%
1405963
 
4.0%
1015537
 
3.7%
1165177
 
3.5%
904890
 
3.3%
1704791
 
3.2%
1054457
 
3.0%
Other values (556)83894
55.9%
ValueCountFrequency (%)
012829
8.6%
18
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
49
 
< 0.1%
542
 
< 0.1%
65
 
< 0.1%
73
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
193121
< 0.1%
179321
< 0.1%
177001
< 0.1%
174101
< 0.1%
173221
< 0.1%
170111
< 0.1%
163121
< 0.1%
140091
< 0.1%
136361
< 0.1%
126841
< 0.1%

kilometer
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.59716
Minimum0.5
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:07.924082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile4
Q112.5
median15
Q315
95-th percentile15
Maximum15
Range14.5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.919575532
Coefficient of variation (CV)0.3111475549
Kurtosis1.141934188
Mean12.59716
Median Absolute Deviation (MAD)0
Skewness-1.525921365
Sum1889574
Variance15.36307235
MonotonicityNot monotonic
2021-12-17T21:23:08.166229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1596877
64.6%
12.515722
 
10.5%
106459
 
4.3%
95257
 
3.5%
84573
 
3.0%
74084
 
2.7%
63725
 
2.5%
53144
 
2.1%
42718
 
1.8%
32501
 
1.7%
Other values (3)4940
 
3.3%
ValueCountFrequency (%)
0.51840
 
1.2%
1746
 
0.5%
22354
1.6%
32501
1.7%
42718
1.8%
53144
2.1%
63725
2.5%
74084
2.7%
84573
3.0%
95257
3.5%
ValueCountFrequency (%)
1596877
64.6%
12.515722
 
10.5%
106459
 
4.3%
95257
 
3.5%
84573
 
3.0%
74084
 
2.7%
63725
 
2.5%
53144
 
2.1%
42718
 
1.8%
32501
 
1.7%

notRepairedDamage
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing24324
Missing (%)16.2%
Memory size1.1 MiB
0.0
111361 
1.0
14315 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0111361
74.2%
1.014315
 
9.5%
(Missing)24324
 
16.2%

Length

2021-12-17T21:23:08.521021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T21:23:08.711009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0111361
88.6%
1.014315
 
11.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

regionCode
Real number (ℝ≥0)

Distinct7905
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2583.077267
Minimum0
Maximum8120
Zeros63
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:08.925263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile188
Q11018
median2196
Q33843
95-th percentile6244.05
Maximum8120
Range8120
Interquartile range (IQR)2825

Descriptive statistics

Standard deviation1885.363218
Coefficient of variation (CV)0.7298903685
Kurtosis-0.3408317795
Mean2583.077267
Median Absolute Deviation (MAD)1349
Skewness0.6888811779
Sum387461590
Variance3554594.464
MonotonicityNot monotonic
2021-12-17T21:23:09.257766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
419369
 
0.2%
764258
 
0.2%
125137
 
0.1%
176136
 
0.1%
462134
 
0.1%
428132
 
0.1%
1184130
 
0.1%
24130
 
0.1%
122129
 
0.1%
828126
 
0.1%
Other values (7895)148319
98.9%
ValueCountFrequency (%)
063
< 0.1%
117
 
< 0.1%
226
 
< 0.1%
330
< 0.1%
465
< 0.1%
539
< 0.1%
640
< 0.1%
77
 
< 0.1%
825
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
81201
< 0.1%
81171
< 0.1%
81131
< 0.1%
81121
< 0.1%
81091
< 0.1%
81071
< 0.1%
81061
< 0.1%
81051
< 0.1%
81041
< 0.1%
81032
< 0.1%

seller
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
149999 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0149999
> 99.9%
11
 
< 0.1%

Length

2021-12-17T21:23:12.846892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T21:23:12.970543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0149999
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

offerType
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
150000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0150000
100.0%

Length

2021-12-17T21:23:13.116093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T21:23:13.262668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0150000
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

creatDate
Real number (ℝ≥0)

SKEWED

Distinct96
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20160330.79
Minimum20150618
Maximum20160407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:13.407765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20150618
5-th percentile20160307
Q120160313
median20160321
Q320160329
95-th percentile20160404
Maximum20160407
Range9789
Interquartile range (IQR)16

Descriptive statistics

Standard deviation106.7328088
Coefficient of variation (CV)5.294199283 × 10-6
Kurtosis6881.080328
Mean20160330.79
Median Absolute Deviation (MAD)8
Skewness-79.01331042
Sum3.024049619 × 1012
Variance11391.89248
MonotonicityNot monotonic
2021-12-17T21:23:13.688849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201604035848
 
3.9%
201604045606
 
3.7%
201603205485
 
3.7%
201603125383
 
3.6%
201604025382
 
3.6%
201603215361
 
3.6%
201603145278
 
3.5%
201603285218
 
3.5%
201603195213
 
3.5%
201603075203
 
3.5%
Other values (86)96023
64.0%
ValueCountFrequency (%)
201506181
< 0.1%
201508071
< 0.1%
201508101
< 0.1%
201509042
< 0.1%
201509091
< 0.1%
201511081
< 0.1%
201511121
< 0.1%
201511171
< 0.1%
201511232
< 0.1%
201511242
< 0.1%
ValueCountFrequency (%)
20160407223
 
0.1%
20160406505
 
0.3%
201604051688
 
1.1%
201604045606
3.7%
201604035848
3.9%
201604025382
3.6%
201604015092
3.4%
201603314769
3.2%
201603305075
3.4%
201603295106
3.4%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3763
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5923.327333
Minimum11
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:13.992192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile400
Q11300
median3250
Q37700
95-th percentile19970
Maximum99999
Range99988
Interquartile range (IQR)6400

Descriptive statistics

Standard deviation7501.998477
Coefficient of variation (CV)1.266517627
Kurtosis18.99518336
Mean5923.327333
Median Absolute Deviation (MAD)2351
Skewness3.346486763
Sum888499100
Variance56279981.14
MonotonicityNot monotonic
2021-12-17T21:23:14.242403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5002337
 
1.6%
15002158
 
1.4%
12001922
 
1.3%
10001850
 
1.2%
25001821
 
1.2%
6001535
 
1.0%
35001533
 
1.0%
8001513
 
1.0%
20001378
 
0.9%
9991356
 
0.9%
Other values (3753)132597
88.4%
ValueCountFrequency (%)
112
 
< 0.1%
123
 
< 0.1%
136
 
< 0.1%
141
 
< 0.1%
1513
< 0.1%
161
 
< 0.1%
171
 
< 0.1%
2014
< 0.1%
2515
< 0.1%
291
 
< 0.1%
ValueCountFrequency (%)
999995
< 0.1%
999901
 
< 0.1%
999001
 
< 0.1%
984301
 
< 0.1%
980002
 
< 0.1%
975001
 
< 0.1%
954001
 
< 0.1%
950001
 
< 0.1%
939001
 
< 0.1%
934901
 
< 0.1%

v_0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143997
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.40626753
Minimum30.45197649
Maximum52.30417826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:14.505619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30.45197649
5-th percentile40.9957098
Q143.13579888
median44.61026572
Q346.0047209
95-th percentile47.74725825
Maximum52.30417826
Range21.85220177
Interquartile range (IQR)2.868922015

Descriptive statistics

Standard deviation2.457547906
Coefficient of variation (CV)0.05534236591
Kurtosis3.993841049
Mean44.40626753
Median Absolute Deviation (MAD)1.431596579
Skewness-1.316712325
Sum6660940.13
Variance6.039541711
MonotonicityNot monotonic
2021-12-17T21:23:14.734619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.3491146920
 
< 0.1%
48.087216516
 
< 0.1%
47.8403568915
 
< 0.1%
47.5684503115
 
< 0.1%
48.2514302315
 
< 0.1%
48.6181496215
 
< 0.1%
48.2659073314
 
< 0.1%
47.8029890514
 
< 0.1%
48.4334397214
 
< 0.1%
47.6664440212
 
< 0.1%
Other values (143987)149850
99.9%
ValueCountFrequency (%)
30.451976491
< 0.1%
30.60739421
< 0.1%
30.827899381
< 0.1%
30.82930161
< 0.1%
31.002200291
< 0.1%
31.133322261
< 0.1%
31.16724671
< 0.1%
31.195995971
< 0.1%
31.204724041
< 0.1%
31.357493591
< 0.1%
ValueCountFrequency (%)
52.304178261
< 0.1%
51.881516561
< 0.1%
51.815421361
< 0.1%
51.712444761
< 0.1%
51.704406411
< 0.1%
51.685336661
< 0.1%
51.671109721
< 0.1%
51.537028371
< 0.1%
51.503280041
< 0.1%
51.499041191
< 0.1%

v_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143998
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04480912301
Minimum-4.295588903
Maximum7.320308375
Zeros0
Zeros (%)0.0%
Negative84634
Negative (%)56.4%
Memory size1.1 MiB
2021-12-17T21:23:14.982257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.295588903
5-th percentile-3.315105571
Q1-3.192349286
median-3.052671416
Q34.000669795
95-th percentile5.156493473
Maximum7.320308375
Range11.61589728
Interquartile range (IQR)7.193019081

Descriptive statistics

Standard deviation3.641893018
Coefficient of variation (CV)-81.27570398
Kurtosis-1.753016996
Mean-0.04480912301
Median Absolute Deviation (MAD)0.2473609618
Skewness0.3594542872
Sum-6721.368452
Variance13.26338475
MonotonicityNot monotonic
2021-12-17T21:23:15.249008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.24513302720
 
< 0.1%
3.18332271116
 
< 0.1%
3.35494927215
 
< 0.1%
2.79673868315
 
< 0.1%
1.94273192315
 
< 0.1%
3.11668778415
 
< 0.1%
3.19536316714
 
< 0.1%
2.88685838914
 
< 0.1%
3.39624399814
 
< 0.1%
3.40029463212
 
< 0.1%
Other values (143988)149850
99.9%
ValueCountFrequency (%)
-4.2955889031
< 0.1%
-4.2369042171
< 0.1%
-4.2002435041
< 0.1%
-4.1698942831
< 0.1%
-4.114646811
< 0.1%
-4.1136371161
< 0.1%
-4.0948068221
< 0.1%
-4.0923142831
< 0.1%
-4.0767733531
< 0.1%
-4.0663821721
< 0.1%
ValueCountFrequency (%)
7.3203083751
< 0.1%
7.2996018311
< 0.1%
7.2704477551
< 0.1%
7.2513852231
< 0.1%
7.1895988721
< 0.1%
7.0943145271
< 0.1%
7.0732861211
< 0.1%
7.0169777441
< 0.1%
7.0058860411
< 0.1%
6.9905453621
< 0.1%

v_2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143997
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08076505845
Minimum-4.47067143
Maximum19.0354965
Zeros0
Zeros (%)0.0%
Negative99360
Negative (%)66.2%
Memory size1.1 MiB
2021-12-17T21:23:15.494891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.47067143
5-th percentile-1.78064028
Q1-0.9706712005
median-0.3829468904
Q30.2413348522
95-th percentile1.228078873
Maximum19.0354965
Range23.50616793
Interquartile range (IQR)1.212006053

Descriptive statistics

Standard deviation2.929617945
Coefficient of variation (CV)36.27333405
Kurtosis23.86059102
Mean0.08076505845
Median Absolute Deviation (MAD)0.605197914
Skewness4.842555904
Sum12114.75877
Variance8.582661304
MonotonicityNot monotonic
2021-12-17T21:23:15.751462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.349859950620
 
< 0.1%
0.826576765916
 
< 0.1%
0.980616481415
 
< 0.1%
0.887762497715
 
< 0.1%
-0.158364141815
 
< 0.1%
0.030707602515
 
< 0.1%
-0.128923062814
 
< 0.1%
0.985872358614
 
< 0.1%
-0.028164446114
 
< 0.1%
0.957995500712
 
< 0.1%
Other values (143987)149850
99.9%
ValueCountFrequency (%)
-4.470671431
< 0.1%
-4.1557429051
< 0.1%
-4.0225342991
< 0.1%
-3.7413925531
< 0.1%
-3.7379704212
< 0.1%
-3.686771561
< 0.1%
-3.6822509721
< 0.1%
-3.6753450931
< 0.1%
-3.5077590031
< 0.1%
-3.4890021911
< 0.1%
ValueCountFrequency (%)
19.03549651
< 0.1%
18.994308911
< 0.1%
18.802117711
< 0.1%
18.676413951
< 0.1%
18.656750781
< 0.1%
18.656047421
< 0.1%
18.628939231
< 0.1%
18.608435571
< 0.1%
18.591427161
< 0.1%
18.578705971
< 0.1%

v_3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143998
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07883342346
Minimum-7.275036707
Maximum9.854701534
Zeros0
Zeros (%)0.0%
Negative72655
Negative (%)48.4%
Memory size1.1 MiB
2021-12-17T21:23:16.004368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7.275036707
5-th percentile-3.173722911
Q1-1.462580044
median0.09972198465
Q31.565838202
95-th percentile3.34599903
Maximum9.854701534
Range17.12973824
Interquartile range (IQR)3.028418246

Descriptive statistics

Standard deviation2.026514036
Coefficient of variation (CV)25.70627973
Kurtosis-0.4180058877
Mean0.07883342346
Median Absolute Deviation (MAD)1.511755874
Skewness0.1062920405
Sum11825.01352
Variance4.106759138
MonotonicityNot monotonic
2021-12-17T21:23:16.242121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.218200512320
 
< 0.1%
-1.31298998216
 
< 0.1%
-1.61987292515
 
< 0.1%
-1.6124324515
 
< 0.1%
-2.00661231415
 
< 0.1%
-1.38275605315
 
< 0.1%
-1.69095303514
 
< 0.1%
-1.71350925714
 
< 0.1%
-1.88184968114
 
< 0.1%
-1.19948773912
 
< 0.1%
Other values (143988)149850
99.9%
ValueCountFrequency (%)
-7.2750367071
< 0.1%
-5.8170294591
< 0.1%
-5.500691721
< 0.1%
-5.4550906721
< 0.1%
-5.4162551611
< 0.1%
-5.411330441
< 0.1%
-5.4097224171
< 0.1%
-5.4006671171
< 0.1%
-5.3313712911
< 0.1%
-5.3271605951
< 0.1%
ValueCountFrequency (%)
9.8547015341
< 0.1%
9.3692268641
< 0.1%
9.1815250541
< 0.1%
9.1215675291
< 0.1%
9.1026539621
< 0.1%
9.0262759041
< 0.1%
8.8632993111
< 0.1%
8.7878278051
< 0.1%
8.6994576211
< 0.1%
8.6728583561
< 0.1%

v_4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143998
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01787461475
Minimum-4.364565242
Maximum6.82935164
Zeros0
Zeros (%)0.0%
Negative77887
Negative (%)51.9%
Memory size1.1 MiB
2021-12-17T21:23:16.512923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.364565242
5-th percentile-1.694681469
Q1-0.9211914838
median-0.07591042907
Q30.8687584354
95-th percentile2.100814569
Maximum6.82935164
Range11.19391688
Interquartile range (IQR)1.789949919

Descriptive statistics

Standard deviation1.193661387
Coefficient of variation (CV)66.77969868
Kurtosis-0.1972952432
Mean0.01787461475
Median Absolute Deviation (MAD)0.8881576237
Skewness0.3679889702
Sum2681.192212
Variance1.424827506
MonotonicityNot monotonic
2021-12-17T21:23:16.732084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.62682823320
 
< 0.1%
0.696775176116
 
< 0.1%
1.2354578615
 
< 0.1%
-0.364986222215
 
< 0.1%
-0.365899502515
 
< 0.1%
-0.379815392815
 
< 0.1%
-0.393283208114
 
< 0.1%
-0.304814687414
 
< 0.1%
1.61713592714
 
< 0.1%
-0.291028604512
 
< 0.1%
Other values (143988)149850
99.9%
ValueCountFrequency (%)
-4.3645652421
< 0.1%
-4.2336259661
< 0.1%
-4.2115009951
< 0.1%
-4.1561681641
< 0.1%
-4.0955550321
< 0.1%
-4.0683869961
< 0.1%
-4.0525962161
< 0.1%
-4.0474080721
< 0.1%
-4.0319932631
< 0.1%
-4.0259809361
< 0.1%
ValueCountFrequency (%)
6.829351641
< 0.1%
4.959093981
< 0.1%
4.9554698031
< 0.1%
4.877270691
< 0.1%
4.8465670741
< 0.1%
4.7734843721
< 0.1%
4.7724064851
< 0.1%
4.7378061041
< 0.1%
4.7187813511
< 0.1%
4.7011439251
< 0.1%

v_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct139624
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2482035284
Minimum0
Maximum0.2918381131
Zeros4485
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:16.968009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2270593411
Q10.2436153531
median0.2577979663
Q30.2652972593
95-th percentile0.2779034901
Maximum0.2918381131
Range0.2918381131
Interquartile range (IQR)0.02168190624

Descriptive statistics

Standard deviation0.04580397102
Coefficient of variation (CV)0.1845419818
Kurtosis22.93408106
Mean0.2482035284
Median Absolute Deviation (MAD)0.009677048151
Skewness-4.737093909
Sum37230.52926
Variance0.002098003761
MonotonicityNot monotonic
2021-12-17T21:23:17.194950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04485
 
3.0%
0.269405813220
 
< 0.1%
0.256226431816
 
< 0.1%
0.265763855315
 
< 0.1%
0.260995784115
 
< 0.1%
0.277096619715
 
< 0.1%
0.261082081615
 
< 0.1%
0.26312581814
 
< 0.1%
0.277397421514
 
< 0.1%
0.260631374614
 
< 0.1%
Other values (139614)145377
96.9%
ValueCountFrequency (%)
04485
3.0%
1.182991951 × 10-51
 
< 0.1%
3.541627639 × 10-51
 
< 0.1%
4.983770368 × 10-51
 
< 0.1%
9.987459124 × 10-51
 
< 0.1%
0.00013198842731
 
< 0.1%
0.00013418999071
 
< 0.1%
0.00013894302131
 
< 0.1%
0.00022953794762
 
< 0.1%
0.00024576889321
 
< 0.1%
ValueCountFrequency (%)
0.29183811311
< 0.1%
0.29162210471
< 0.1%
0.2914730691
< 0.1%
0.29143128431
< 0.1%
0.2912938371
< 0.1%
0.29121634951
< 0.1%
0.29120074021
< 0.1%
0.29117556111
< 0.1%
0.29111231391
< 0.1%
0.29108927381
< 0.1%

v_6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct109766
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04492300431
Minimum0
Maximum0.1514195959
Zeros35465
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:17.455975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.811099819 × 10-5
median0.0008120586185
Q30.1020092977
95-th percentile0.119640585
Maximum0.1514195959
Range0.1514195959
Interquartile range (IQR)0.1019711867

Descriptive statistics

Standard deviation0.05174278749
Coefficient of variation (CV)1.151810488
Kurtosis-1.742566654
Mean0.04492300431
Median Absolute Deviation (MAD)0.0008120586185
Skewness0.3680730421
Sum6738.450646
Variance0.002677316057
MonotonicityNot monotonic
2021-12-17T21:23:17.726047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035465
 
23.6%
5.281627533 × 10-520
 
< 0.1%
0.0934723056316
 
< 0.1%
0.0752118678115
 
< 0.1%
0.0918874552915
 
< 0.1%
0.0950288338515
 
< 0.1%
0.0875616372315
 
< 0.1%
0.0928530516114
 
< 0.1%
0.0887036155314
 
< 0.1%
0.0954522984414
 
< 0.1%
Other values (109756)114397
76.3%
ValueCountFrequency (%)
035465
23.6%
1.498903956 × 10-81
 
< 0.1%
1.855559964 × 10-81
 
< 0.1%
3.458416163 × 10-81
 
< 0.1%
5.365260697 × 10-81
 
< 0.1%
6.360355517 × 10-81
 
< 0.1%
8.652398178 × 10-81
 
< 0.1%
1.114472255 × 10-71
 
< 0.1%
1.211383514 × 10-71
 
< 0.1%
1.306585345 × 10-71
 
< 0.1%
ValueCountFrequency (%)
0.15141959591
< 0.1%
0.15110577131
< 0.1%
0.15038921331
< 0.1%
0.15032204621
< 0.1%
0.14991015031
< 0.1%
0.1495848721
< 0.1%
0.14955215971
< 0.1%
0.1489944721
< 0.1%
0.14832837261
< 0.1%
0.14788613861
< 0.1%

v_7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct138709
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1246924611
Minimum0
Maximum1.404936375
Zeros5467
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:17.997904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007337314046
Q10.06247353269
median0.09586589827
Q30.1252429449
95-th percentile0.1681911025
Maximum1.404936375
Range1.404936375
Interquartile range (IQR)0.06276941225

Descriptive statistics

Standard deviation0.2014095303
Coefficient of variation (CV)1.615250261
Kurtosis25.84548929
Mean0.1246924611
Median Absolute Deviation (MAD)0.03105539056
Skewness5.130233019
Sum18703.86917
Variance0.04056579891
MonotonicityNot monotonic
2021-12-17T21:23:18.264091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05467
 
3.6%
0.124212572220
 
< 0.1%
0.130271918216
 
< 0.1%
0.0516797522815
 
< 0.1%
0.0815952102415
 
< 0.1%
0.13966734415
 
< 0.1%
0.140907486915
 
< 0.1%
0.140864800314
 
< 0.1%
0.045171096214
 
< 0.1%
0.0729295705814
 
< 0.1%
Other values (138699)144395
96.3%
ValueCountFrequency (%)
05467
3.6%
8.788703506 × 10-71
 
< 0.1%
4.967235703 × 10-61
 
< 0.1%
5.313628437 × 10-61
 
< 0.1%
7.115923657 × 10-61
 
< 0.1%
9.02865141 × 10-61
 
< 0.1%
1.028944288 × 10-51
 
< 0.1%
1.411220448 × 10-51
 
< 0.1%
1.482224932 × 10-51
 
< 0.1%
1.720118199 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1.4049363751
< 0.1%
1.4019990121
< 0.1%
1.3878465941
< 0.1%
1.3853667871
< 0.1%
1.3729341791
< 0.1%
1.3717882111
< 0.1%
1.3610389281
< 0.1%
1.3558474551
< 0.1%
1.3502932721
< 0.1%
1.3493057511
< 0.1%

v_8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct142451
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05814385476
Minimum0
Maximum0.1607909853
Zeros1597
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:18.532646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01353156407
Q10.03533368675
median0.05701359764
Q30.07938157081
95-th percentile0.1087993996
Maximum0.1607909853
Range0.1607909853
Interquartile range (IQR)0.04404788406

Descriptive statistics

Standard deviation0.02918575568
Coefficient of variation (CV)0.5019577013
Kurtosis-0.6362252576
Mean0.05814385476
Median Absolute Deviation (MAD)0.02199898145
Skewness0.204613257
Sum8721.578214
Variance0.0008518083346
MonotonicityNot monotonic
2021-12-17T21:23:18.768866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01597
 
1.1%
0.0673580088820
 
< 0.1%
0.0747416888316
 
< 0.1%
0.0846955635215
 
< 0.1%
0.073268143315
 
< 0.1%
0.0766635564515
 
< 0.1%
0.0759046805915
 
< 0.1%
0.0786583525914
 
< 0.1%
0.0753755761614
 
< 0.1%
0.0805840454414
 
< 0.1%
Other values (142441)148265
98.8%
ValueCountFrequency (%)
01597
1.1%
9.229282312 × 10-71
 
< 0.1%
2.029666399 × 10-61
 
< 0.1%
4.413213728 × 10-61
 
< 0.1%
6.403629719 × 10-61
 
< 0.1%
7.995614707 × 10-61
 
< 0.1%
1.003058269 × 10-51
 
< 0.1%
1.39975955 × 10-51
 
< 0.1%
2.333513684 × 10-51
 
< 0.1%
2.457165374 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.16079098531
< 0.1%
0.15970960241
< 0.1%
0.15776628131
< 0.1%
0.15557614191
< 0.1%
0.15384940681
< 0.1%
0.15349777511
< 0.1%
0.15344392231
< 0.1%
0.15323942521
< 0.1%
0.15231508651
< 0.1%
0.15208497651
< 0.1%

v_9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct140617
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0619958947
Minimum0
Maximum0.2227874876
Zeros3486
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2021-12-17T21:23:19.024807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009496496083
Q10.03393017697
median0.05848366702
Q30.08749054835
95-th percentile0.1248414393
Maximum0.2227874876
Range0.2227874876
Interquartile range (IQR)0.05356037137

Descriptive statistics

Standard deviation0.03569197873
Coefficient of variation (CV)0.575715197
Kurtosis-0.3214911789
Mean0.0619958947
Median Absolute Deviation (MAD)0.02654984894
Skewness0.4195007497
Sum9299.384205
Variance0.001273917346
MonotonicityNot monotonic
2021-12-17T21:23:19.260729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03486
 
2.3%
0.0148667291520
 
< 0.1%
0.0827647696816
 
< 0.1%
0.0458773960615
 
< 0.1%
0.0467653032515
 
< 0.1%
0.101149737815
 
< 0.1%
0.0515351016715
 
< 0.1%
0.0496107149814
 
< 0.1%
0.0482027481614
 
< 0.1%
0.1108865714
 
< 0.1%
Other values (140607)146376
97.6%
ValueCountFrequency (%)
03486
2.3%
1.271994569 × 10-61
 
< 0.1%
3.552864466 × 10-61
 
< 0.1%
5.529589418 × 10-61
 
< 0.1%
7.708846052 × 10-61
 
< 0.1%
1.116667106 × 10-51
 
< 0.1%
1.12864626 × 10-51
 
< 0.1%
1.168502825 × 10-51
 
< 0.1%
1.377531526 × 10-51
 
< 0.1%
1.44913339 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.22278748761
< 0.1%
0.21361695911
< 0.1%
0.21176917371
< 0.1%
0.2110746741
< 0.1%
0.21040216271
< 0.1%
0.20771643211
< 0.1%
0.20582369211
< 0.1%
0.20573411121
< 0.1%
0.20163342751
< 0.1%
0.20141281921
< 0.1%

v_10
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143997
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.001000238809
Minimum-9.16819241
Maximum12.35701062
Zeros0
Zeros (%)0.0%
Negative63813
Negative (%)42.5%
Memory size1.1 MiB
2021-12-17T21:23:19.502214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-9.16819241
5-th percentile-5.65890342
Q1-3.72230288
median1.624076331
Q32.844356776
95-th percentile4.056172056
Maximum12.35701062
Range21.52520303
Interquartile range (IQR)6.566659656

Descriptive statistics

Standard deviation3.772386394
Coefficient of variation (CV)-3771.485731
Kurtosis-0.5779350635
Mean-0.001000238809
Median Absolute Deviation (MAD)2.148033959
Skewness0.02522046412
Sum-150.0358213
Variance14.23089911
MonotonicityNot monotonic
2021-12-17T21:23:19.726725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.32938640620
 
< 0.1%
-4.30348055216
 
< 0.1%
-3.16323577715
 
< 0.1%
-4.04739992615
 
< 0.1%
-4.75735850315
 
< 0.1%
-4.38392889315
 
< 0.1%
-4.81588449714
 
< 0.1%
-3.95085720514
 
< 0.1%
-4.68644227214
 
< 0.1%
-4.29457831712
 
< 0.1%
Other values (143987)149850
99.9%
ValueCountFrequency (%)
-9.168192411
< 0.1%
-9.1095250991
< 0.1%
-8.7988098731
< 0.1%
-8.7768985451
< 0.1%
-8.5638862871
< 0.1%
-8.5314047121
< 0.1%
-8.4395776631
< 0.1%
-8.4369454251
< 0.1%
-8.4201949431
< 0.1%
-8.4030060641
< 0.1%
ValueCountFrequency (%)
12.357010621
< 0.1%
12.31930271
< 0.1%
12.285298821
< 0.1%
12.180502951
< 0.1%
12.169001071
< 0.1%
12.148415961
< 0.1%
12.146426661
< 0.1%
12.130893071
< 0.1%
12.1183431
< 0.1%
12.103093921
< 0.1%

v_11
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143997
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009034543473
Minimum-5.558206704
Maximum18.81904247
Zeros0
Zeros (%)0.0%
Negative83038
Negative (%)55.4%
Memory size1.1 MiB
2021-12-17T21:23:19.965320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5.558206704
5-th percentile-3.373762442
Q1-1.951543007
median-0.3580526972
Q31.255021657
95-th percentile2.840711833
Maximum18.81904247
Range24.37724917
Interquartile range (IQR)3.206564665

Descriptive statistics

Standard deviation3.286071221
Coefficient of variation (CV)363.7229962
Kurtosis12.56873147
Mean0.009034543473
Median Absolute Deviation (MAD)1.603479007
Skewness3.029145813
Sum1355.181521
Variance10.79826407
MonotonicityNot monotonic
2021-12-17T21:23:20.297540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.25559064420
 
< 0.1%
-0.330053027216
 
< 0.1%
-0.802613557815
 
< 0.1%
-0.0798717039715
 
< 0.1%
-1.10794025415
 
< 0.1%
-1.43649360815
 
< 0.1%
-1.12489246514
 
< 0.1%
-0.305946370814
 
< 0.1%
-1.22980565514
 
< 0.1%
0.0958698500412
 
< 0.1%
Other values (143987)149850
99.9%
ValueCountFrequency (%)
-5.5582067041
< 0.1%
-5.4030442251
< 0.1%
-5.3911541021
< 0.1%
-5.3665801891
< 0.1%
-5.3628609911
< 0.1%
-5.3577510621
< 0.1%
-5.3232577111
< 0.1%
-5.3197423931
< 0.1%
-5.3066717182
< 0.1%
-5.3007440951
< 0.1%
ValueCountFrequency (%)
18.819042471
< 0.1%
18.802071841
< 0.1%
18.801218361
< 0.1%
18.78710181
< 0.1%
18.765443011
< 0.1%
18.758964691
< 0.1%
18.748292671
< 0.1%
18.746229281
< 0.1%
18.744445091
< 0.1%
18.731198911
< 0.1%

v_12
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143997
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004812595252
Minimum-9.639552114
Maximum13.84779152
Zeros0
Zeros (%)0.0%
Negative77843
Negative (%)51.9%
Memory size1.1 MiB
2021-12-17T21:23:20.718463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-9.639552114
5-th percentile-3.790116166
Q1-1.871845761
median-0.1307533175
Q31.776932949
95-th percentile4.100404318
Maximum13.84779152
Range23.48734364
Interquartile range (IQR)3.64877871

Descriptive statistics

Standard deviation2.517477676
Coefficient of variation (CV)523.1018908
Kurtosis0.2689373892
Mean0.004812595252
Median Absolute Deviation (MAD)1.819659384
Skewness0.3653576011
Sum721.8892878
Variance6.33769385
MonotonicityNot monotonic
2021-12-17T21:23:21.113510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.847433065320
 
< 0.1%
2.48629694416
 
< 0.1%
3.09796275915
 
< 0.1%
2.37525568215
 
< 0.1%
2.1044704815
 
< 0.1%
2.51577680915
 
< 0.1%
2.26027318414
 
< 0.1%
2.7697400614
 
< 0.1%
2.38738234314
 
< 0.1%
2.29105357712
 
< 0.1%
Other values (143987)149850
99.9%
ValueCountFrequency (%)
-9.6395521141
< 0.1%
-9.4041058921
< 0.1%
-9.2888950571
< 0.1%
-9.2239925771
< 0.1%
-8.6792900011
< 0.1%
-8.293423151
< 0.1%
-7.9948110061
< 0.1%
-7.9409384521
< 0.1%
-7.8826030271
< 0.1%
-7.7816454791
< 0.1%
ValueCountFrequency (%)
13.847791521
< 0.1%
13.562011371
< 0.1%
13.113042841
< 0.1%
13.083661411
< 0.1%
12.973056811
< 0.1%
12.964502441
< 0.1%
12.869280931
< 0.1%
12.693091791
< 0.1%
12.692200831
< 0.1%
12.533885571
< 0.1%

v_13
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143998
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0003126119439
Minimum-4.153898796
Maximum11.14766861
Zeros0
Zeros (%)0.0%
Negative76475
Negative (%)51.0%
Memory size1.1 MiB
2021-12-17T21:23:21.388565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.153898796
5-th percentile-1.931083984
Q1-1.057788984
median-0.03624460356
Q30.9428130826
95-th percentile2.187794499
Maximum11.14766861
Range15.30156741
Interquartile range (IQR)2.000602067

Descriptive statistics

Standard deviation1.288987639
Coefficient of variation (CV)4123.283402
Kurtosis-0.4382740227
Mean0.0003126119439
Median Absolute Deviation (MAD)1.002843891
Skewness0.2679151914
Sum46.89179159
Variance1.661489135
MonotonicityNot monotonic
2021-12-17T21:23:21.629102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.69849692120
 
< 0.1%
-0.0434626713516
 
< 0.1%
0.54869661415
 
< 0.1%
-0.834697159115
 
< 0.1%
-0.367855255115
 
< 0.1%
-0.49945274615
 
< 0.1%
-0.662800105814
 
< 0.1%
-0.375957369214
 
< 0.1%
1.03732396214
 
< 0.1%
-0.477793505712
 
< 0.1%
Other values (143988)149850
99.9%
ValueCountFrequency (%)
-4.1538987961
< 0.1%
-4.1027684961
< 0.1%
-4.0749224081
< 0.1%
-4.07212171
< 0.1%
-4.0697420291
< 0.1%
-4.0675990181
< 0.1%
-4.0619256272
< 0.1%
-4.0569647191
< 0.1%
-4.0515947481
< 0.1%
-4.0506475381
< 0.1%
ValueCountFrequency (%)
11.147668611
< 0.1%
5.2497497851
< 0.1%
5.2495721371
< 0.1%
5.232337221
< 0.1%
5.2286021351
< 0.1%
5.2115314281
< 0.1%
5.2022672491
< 0.1%
5.1548222121
< 0.1%
5.1487952431
< 0.1%
5.1407870751
< 0.1%

v_14
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct143998
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0006882314377
Minimum-6.546555965
Maximum8.658417877
Zeros0
Zeros (%)0.0%
Negative63783
Negative (%)42.5%
Memory size1.1 MiB
2021-12-17T21:23:22.081706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-6.546555965
5-th percentile-2.109160871
Q1-0.4370336682
median0.1412459925
Q30.6803780745
95-th percentile1.365639579
Maximum8.658417877
Range15.20497384
Interquartile range (IQR)1.117411743

Descriptive statistics

Standard deviation1.038685151
Coefficient of variation (CV)-1509.209104
Kurtosis2.393525934
Mean-0.0006882314377
Median Absolute Deviation (MAD)0.5573812386
Skewness-1.186355325
Sum-103.2347157
Variance1.078866844
MonotonicityNot monotonic
2021-12-17T21:23:22.344976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00301493659920
 
< 0.1%
-2.29034371516
 
< 0.1%
-3.05944411915
 
< 0.1%
-1.33041248415
 
< 0.1%
1.0274867315
 
< 0.1%
0.869586171115
 
< 0.1%
-0.550825834814
 
< 0.1%
0.979706133414
 
< 0.1%
-2.48878148514
 
< 0.1%
1.16244764812
 
< 0.1%
Other values (143988)149850
99.9%
ValueCountFrequency (%)
-6.5465559651
< 0.1%
-6.1247084981
< 0.1%
-6.1232919141
< 0.1%
-6.1132913041
< 0.1%
-6.0661073981
< 0.1%
-6.0637696591
< 0.1%
-6.0542161291
< 0.1%
-6.0422934991
< 0.1%
-6.0390438111
< 0.1%
-6.032871481
< 0.1%
ValueCountFrequency (%)
8.6584178771
< 0.1%
2.7439927171
< 0.1%
2.7294424871
< 0.1%
2.6973647261
< 0.1%
2.6288654621
< 0.1%
2.6101312281
< 0.1%
2.5970031081
< 0.1%
2.5944836251
< 0.1%
2.5896900851
< 0.1%
2.5892340921
< 0.1%

Interactions

2021-12-17T21:22:50.048389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:11.518827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:20.003780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:28.077741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:36.972276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:44.396886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:52.012196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:00.649645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:10.037248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:21.150858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:30.896434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:39.894851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:50.077054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:58.635653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:06.416745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:14.709706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:21.870684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:29.217422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:36.499268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:44.529834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:53.157087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:01.790432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:10.458531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:17.696297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:27.516862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:35.142369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:43.105473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:50.304294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:11.854193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:20.290191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:28.348613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:37.248961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:44.653594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:52.293768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:00.916935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:10.314083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:21.483959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:31.300217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:40.167988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:50.393381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:58.938959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:06.662149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:15.038145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:22.133292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:29.500789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:36.762728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:44.796784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:54.412718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:02.176597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:10.718335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:17.937487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:27.770774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:35.585580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:43.358416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:50.563200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:12.159787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:20.578332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:28.747124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:37.524297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:44.928659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:52.574474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:01.200004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:10.586565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:22.075606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:31.741077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:42.006420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:50.812299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:59.200288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:06.915539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:15.396968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:22.396933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:29.757813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:37.062355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:45.206124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:54.772294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:02.461306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:10.973547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:18.228319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:28.061383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:35.995454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:43.614756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:50.837319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:12.455982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:20.876206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:29.355564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:37.833848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:45.193872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:52.871006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:01.464772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:10.884522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:22.401856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:32.054004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:42.360716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:51.117736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:59.478477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:07.153342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:15.708274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:22.653983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:30.013483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:37.320518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:45.669902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:55.154001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:02.827730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:11.208370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:18.482714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:28.340891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:36.412169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:43.871263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:51.150659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:12.750667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:21.181662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:29.605836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:38.084363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:45.436960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:19:53.166968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:01.840665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:11.153095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:22.719631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:32.505042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:42.649711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:51.391091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:20:59.759790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:07.387418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:15.964342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:22.906239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:30.260874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:37.565146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:46.079769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:21:55.406480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:03.078221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:11.434827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:18.710268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:28.617836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:36.667034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T21:22:44.111310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-17T21:22:49.799368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-17T21:23:23.384483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-17T21:23:24.137037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-17T21:23:25.747512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-17T21:23:26.125548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-17T21:22:58.505081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-17T21:23:00.678491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-17T21:23:02.249443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-17T21:23:02.723197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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